The choice of \(q_\phi \)  is only limited by three requirements. First of all, we need to be able to sample from \(q_\phi \) . Secondly, we need an expression for the regularizer \(D_{\mathrm {KL}}(q_{\phi }(\theta )\Vert \pi (\theta ))\)  - either explicitly or via Monte Carlo Sampling [1]}. Finally, we have to be able to optimize the arising loss function \(\mathcal {L}(\phi ,\sigma ^2)\)  w.r.t. \(\phi \)  for example via the reparametrization trick [1]}. Merely for convenience we will restrict ourselve in this work to the popular choice of Monte Carlo dropout [3]} where \(q_\phi \)  arises from dropping randomly nodes of the network with some probability and where \(\phi \)  simply coincides with the network parameters. However, let us emphasize that the algorithm described in this work is by no means restricted to this particular choice but is usable for any \(q_\phi \)  for which the above applies.
